Drug Repositioning Ketamine as a New Treatment for Bipolar Disorder Using Text Mining

Abstract

Bipolar Disorder (BD), a chronic mental illness, does not have an ideal treatment, and patients with BD have a higher chance of being diagnosed with alcohol abuse, liver disease, and diabetes. The goal of treatment is to prevent a relapse in BD episodes and find a new treatment. The research here looks at the genetics of BD and ignores environmental factors, as they are subjective. Therapy treats known environmental triggers and stressors and explores methods to reduce them. However, therapy alone cannot fully alleviate the symptoms of BD. My research employs text-mining as a primary strategy to obtain relevant genes and drugs pertaining to BD. The main gene involved is the Brain-Derived Neurotrophic Factor (BDNF). Popular drugs currently used for treatment of BD are Lithium and Carbamazepine. Using CMapPy to look at gene expression data, one sees a relationship between the two drug therapies and BDNF. Lithium fails to treat mania and Carbamazepine fails to treat depression, relatively speaking. When comparing gene expression data of Lithium and Carbamazepine with Ketamine, a newer therapy for BD, Ketamine, raises the BDNF level, keeps it elevated, and effectively controls BD episodes. Ketamine does not have the shortcomings that Lithium and Carbamazepine have. Next steps would include conducting a clinical trial with the hopeful application of Ketamine as a new treatment for BD.